,A double-layered nonlinear model predictive control based control algorithm for local trajectory pl

• 作 者: Hong-chao WANG Wei-wei ZHANG Xun-cheng WU Hao-tian CAO Qiao-ming GAO Su-yun LUO
• 机 构: College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shangh
• 出 处: 信息与电子工程前沿（英文版）
• 发表日期: 2020年7期
• 关键词: Automated truck Trajectory planning Nonlinear model predictive control Longitudi

We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynami-cally feasible trajectory, and the SLN-MPC controller is designed to limit the longitudinal slip of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance opera-tion can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.